With increasing volumes of data stored and processed in clusters such as the Cloud, analytics over such data is becoming very expensive. For example, a pay-as-you-go paradigm associated with the Cloud may cause computation costs to increase linearly with query execution time, making it possible for a data scientist to easily spend large amounts of money analyzing data. The expense may be exacerbated by the exploratory nature of analytics, where queries are iteratively discovered and refined, including the submission of many off-target and erroneous queries (e.g., faulty parameters). In conventional systems, queries and other computations may need to execute to completion before such issues are diagnosed, often after hours of expensive computation time are exhausted.